Senior Data Scientist

About Sugar.fit

Founded in 2021, Sugar.fit provides a personalized, evidence-based path to type 2 and pre-diabetes reversal and management. Leveraging a precision health platform that combines CGM sensors, machine learning, and medical science, we help improve metabolic health through precision nutrition, progressive fitness, and behavioral health coaching. Our research-driven treatment plan blends innovative technology, compassionate diabetes experts, and personalized strategies to empower people to lead a normal, healthy life.

Function: Data Science and Analysis – Data Science / Machine Learning

Responsibilities

• Lead and mentor a team of data scientists and analysts, providing technical guidance, career development, and performance feedback.
• Collaborate with cross-functional teams to align data science initiatives with business objectives.
• Analyze raw data: assess quality, cleanse, and structure it for downstream processing.
• Design and deploy accurate, scalable, and high-performance prediction algorithms.
• Drive end-to-end ownership of machine learning models, from research to production.
• Collaborate with engineering to transition analytical prototypes into production-ready solutions.
• Generate actionable insights for business strategy and product improvements.
• Stay updated on emerging technologies and industry trends, introducing best practices to the team.

Requirements

• Experience: 4-7 years in quantitative analytics, data modeling, or machine learning, including 2+ years of team leadership/management experience.
• Strong problem-solving skills with a focus on product development.
• Expertise in statistical programming languages (Python, R, SQL, etc.) to manipulate and analyze large datasets.
• Strong understanding of a variety of machine learning techniques (clustering, decision trees, neural networks, NLP, etc.) and their practical pros/cons.
• Proven track record of deploying ML models at scale in production environments.
• Excellent communication skills for coordinating across teams and presenting to leadership.
• Proficiency in Big Data frameworks and visualization tools (Hadoop, Spark, Cassandra, Tableau, Power BI, etc.).
• Familiarity with cloud environments (AWS, GCP, Azure) is a plus.
• Bachelor's/Master's/Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.